A digital camera which stores the image of a scene in the form of digital data is well known. In such a camera, the optical image of the scene is converted to an analog electrical signal in a CMOS or CCD image sensor. The analog signal is converted to digital form for further processing, compression and storage as an image file on a memory card or other storage device incorporated into the camera.
Besides the analog image signal, a dark signal also accumulates due to thermal excitation of electrons within the sensor substrate. Because this phenomenon is most noticeable under no-light conditions, these generated electrons are called “dark current”. As such, a dark current signal is not a random noise, but an error in the form of a specified offset. Thus, corrections for each dark current signal may be made by subtracting values from a dark frame. Nonetheless, a high dark current signal may limit the usable sensitivity of the sensor since the image modulation will begin to get lost in the noise from increased dark current, that is, signal to noise in the shadows will be diminished.
Moreover, the level of dark current is a function of temperature, and the warmth of the image sensor chip directly influences the number of electrons generated. The amount of dark current approximately doubles for every 8° C. increase in temperature. Since the amount of dark current is directly related to each pixel site, mostly by levels of defects, there is a fixed pattern offset signal across a sensor for a given temperature and integration time.
While the offset due to the dark current signal can be corrected by subtracting current values obtained from a dark frame exposed at the same temperature and for the same integration time as the image frame, the effects of dark current are more complicated and cannot be corrected so easily. In a single pixel measurement over time, the amount of dark current generated over several frames will follow a statistical distribution; this variation is commonly called dark current shot noise. This noise arises from the quantum (statistical) nature of the thermal electrons produced in the sensor. Thus, while the fixed pattern noise is easy to subtract, the shot noise is random and will remain to an extent after fixed pattern subtraction.
Consequently, it is known in the literature of dark current noise reduction (see, e.g., the background discussion in United States Patent Application Publication US 2003/0210344 A1) to take multiple dark frame images, usually more than five, average the dark frame images pixel-by-pixel, and then subtract the averaged dark frame from the image frame to create an image with reduced dark current noise. The fixed pattern offset component is automatically corrected by the subtraction and, since the shot noise is statistical, the shot noise is reduced by combining data from the multiple measurements. Essentially, by this approach, shot noise is reduced by a factor equal to the square root of the number of measurements combined.
Furthermore, as referenced in United States Patent Application Publication US 2004/0036775 A1, a known, and similar, technique of noise reduction in relation to image frames is the ensemble averaging of images having the identical exposure time. By this method, a series of subject images having the same exposure time are acquired. The values of corresponding pixels from each of these images are summed and divided by the number of images to compute the arithmetic mean of the image set. As with the dark frame example, this method reduces random noise such as shot noise by the square root of the number of images averaged.
Accordingly, it is well known that solid state image sensors of the type commonly used in consumer digital cameras exhibit dark current which when integrated over the exposure period adds both a fixed pattern offset signal and a random (shot) noise signal to the image signal. Many photographic applications require very long exposure times. As exposure times increase, digital cameras become unusable as image noise increases or the imager becomes saturated with dark current signal. This is a particular problem with the popular complementary metal oxide semiconductor (CMOS) image sensors. For example, while an 8 second exposure may leave adequate headroom (above the dark current level and below the saturation level) for an image signal in a charge coupled device (CCD) image sensor, dark current in a CMOS image sensor will completely saturate the pixels.
Various methods have been incorporated to solve this problem. The most effective method is to capture multiple frames of the image, reading each frame from the imager while integrating the signal for the next frame. The frames are each converted to digital data and summed to produce a final digital image. This method permits an indefinite extension of the exposure time, as the imager is cleared of dark current signal as each frame is read out. Of course, the dark current signal will still accumulate in the image sensor for each exposure but the image signal is now measurable since the exposure time of an individual image frame has been shortened. As a result, there is adequate “headroom” between the noise floor and the level at which the sensor pixels will go into saturation.
While the captured image frames are typically corrected for the remaining dark current signal in one of the aforementioned ways, it would be desirable to have a more robust correction for the random (shot) noise associated with the dark current signal in such situations.